Software apps and online services

We use this for our automated approaches to detect cracks and other street defects

open street maps

Used for data publishing and cleaning.

OSMnx - Python for Street Networks

Geoff Boeing's creation allows us to quickly convert raw GPS data to actionable maps really quickly and in an open manner.

Open Street Cam

We use Telenav's Open Street Cam as a primary source for data collection

Zooniverse

We use the Zooniverse platform to annotate and label street imagery to improve our computer vision models

geopandas

Story

The maintenance of city streets is a most visible indicator of a city government's performance.

Video courtesy Fox News

Every year, US cities spend $ Billions on the maintenance and upkeep of roads. The methods used to survey streets range from:

Citizens calling in potholes on an ad-hoc basis, or reporting via smartphone apps.

“Windshield surveying” where a trained inspector assigns a score to the quality of a street based on his/her judgement along with a scoring methodology that dates back to the '70s. (Pavement Condition Index)

Bulky, military grade equipment which provide an uber-high precision but only for a small sample of streets.

What's missing is a low-cost method to collect data about street quality for ALL streets in a city in a consistent and methodical manner that answers simple question:

Which streets are worse off than others in the city?

In an age of autonomous vehicle, cities and municipalities need digital tools to ensure that their streets are well maintained at at-cost.

To this end, we created SQUID, a low cost data platform that integrates open source technologies to combine street imagery and ride quality data to provide a visual ground truth for all the city's streets.

In New York City's case, that is in excess of 6,000 miles of streets!

Street QUality IDentification

Where SQUID fits in

New York City

Fall 2015

We worked with City of New York's Office of Operations and their SCOUT Team to collect 400+ miles of data from a single vehicle in just over 1 week!

Imagine, if just 15 vehicles vehicles were used to collect street imagery and ride quality data, we could achieve a complete and up-to-date street condition surveys across the entire city in mere weeks!

Pilot with the NYC's SCOUT Team ;

SQUID is a project of ARGO Labs, a non-profit organization that builds, operates, and maintains data infrastructures to help cities deliver core public services better, faster, and cheaper.

SQUID started out as a hardware device, a $30 Raspberry pi fitted with accelerometer and GPS sensors, naive optimism and a persistence to prove the initial hypothesis. After collecting our first dataset on the streets of New York, we set out to polish our "lump of clay".

Syracuse, NY

Between April 14 - 28, 2016, we collected approximately 500 miles of street imagery (over 110,000 images) in just 10 days with little manual intervention, demonstrating the scalability of this approach to inspect an entire city's street infrastructure.

Schematics

Digitizing Municipal Street Inspections - 2016 Data for Good exchange

"People want an authority to tell them how to value things. But they chose this authority not based on facts or results. They chose it because it seems authoritative and familiar." - The Big Short

The pavement condition index is one such a familiar measure used by many US cities to measure street quality and justify billions of dollars spent every year on street repair. These billion-dollar decisions are based on evaluation criteria that are subjective and not representative. In this paper, we build upon our initial submission to D4GX 2015 that approaches this problem of information asymmetry in municipal decision-making. We describe a process to identify street-defects using computer vision techniques on data collected using the Street Quality Identification Device (SQUID). A User Interface to host a large quantity of image data towards digitizing the street inspection process and enabling actionable intelligence for a core public service is also described. This approach of combining device, data and decision-making around street repair enables cities make targeted decisions about street repair and could lead to an anticipatory response which can result in significant cost savings. Lastly, we share lessons learnt from the deployment of SQUID in the city of Syracuse, NY.